{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/101116"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/101116","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Anomaly detection for environmental noise monitoring","abstract":"Octave-band sound pressure level is the preferred measure for continuous environmental noise monitoring over raw audio because accepted standards and devices exist, these data do not compromise voice privacy, and thus an octave-band sound meter can legally collect data in public. By setting up an experiment that continuously monitors octave-band sound pressure level in a residential street, we show daily noise-level patterns correlated to human activities. Directly applying well-known anomaly detection algorithms including one-class support vector machine, replicator neural network, and principal component analysis based anomaly detection shows low performance in the collected data because these standard algorithms are unable to exploit the daily patterns. Therefore, principal component analysis anomaly detection with time-varying mean and the covariance matrix over each hour, is proposed in order to detect abnormal acoustic events in the octave band measurements of the residential-noise-monitoring application. The proposed method performs at 0.83 in recall, 0.88 in precision and 0.85 in F-measure on the evaluation data set.","abstract_html":"Octave-band sound pressure level is the preferred measure for continuous environmental noise monitoring over raw audio because accepted standards and devices exist, these data do not compromise voice privacy, and thus an octave-band sound meter can legally collect data in public. By setting up an experiment that continuously monitors octave-band sound pressure level in a residential street, we show daily noise-level patterns correlated to human activities. Directly applying well-known anomaly detection algorithms including one-class support vector machine, replicator neural network, and principal component analysis based anomaly detection shows low performance in the collected data because these standard algorithms are unable to exploit the daily patterns. Therefore, principal component analysis anomaly detection with time-varying mean and the covariance matrix over each hour, is proposed in order to detect abnormal acoustic events in the octave band measurements of the residential-noise-monitoring application. The proposed method performs at 0.83 in recall, 0.88 in precision and 0.85 in F-measure on the evaluation data set.","abstract_has_math":false,"creators":["Phan, Duc H."],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Jones, Douglas L."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-09-04T20:33:48Z","date_published":"2018-09-04T20:33:48Z","updated_at":"2026-07-22T22:24:38Z","subjects":["Anomaly detection, environmental noise monitoring"],"languages":["en"],"rights":["copyright 2018 Duc Phan"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/101116","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Jones, Douglas L."]},{"key":"dc:creator","label":"Author","values":["Phan, Duc H."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-09-04T20:33:48Z","2018-02-23","2018-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Anomaly detection, environmental noise monitoring"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["copyright 2018 Duc Phan"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/101116"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Octave-band sound pressure level is the preferred measure for continuous environmental noise monitoring over raw audio because accepted standards and devices exist, these data do not compromise voice privacy, and thus an octave-band sound meter can legally collect data in public. By setting up an experiment that continuously monitors octave-band sound pressure level in a residential street, we show daily noise-level patterns correlated to human activities. Directly applying well-known anomaly detection algorithms including one-class support vector machine, replicator neural network, and principal component analysis based anomaly detection shows low performance in the collected data because these standard algorithms are unable to exploit the daily patterns. Therefore, principal component analysis anomaly detection with time-varying mean and the covariance matrix over each hour, is proposed in order to detect abnormal acoustic events in the octave band measurements of the residential-noise-monitoring application. The proposed method performs at 0.83 in recall, 0.88 in precision and 0.85 in F-measure on the evaluation data set.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2020-05-01","The student, Duc Phan, accepted the attached license on 2018-02-22 at 10:07.","The student, Duc Phan, submitted this Thesis for approval on 2018-02-22 at 10:16.","This Thesis was approved for publication on 2018-02-23 at 11:06.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12045 on 2018-08-31 at 17:17:11","Made available in DSpace on 2018-09-04T20:33:48Z (GMT). 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By setting up an experiment that continuously monitors octave-band sound pressure level in a residential street, we show daily noise-level patterns correlated to human activities. Directly applying well-known anomaly detection algorithms including one-class support vector machine, replicator neural network, and principal component analysis based anomaly detection shows low performance in the collected data because these standard algorithms are unable to exploit the daily patterns. Therefore, principal component analysis anomaly detection with time-varying mean and the covariance matrix over each hour, is proposed in order to detect abnormal acoustic events in the octave band measurements of the residential-noise-monitoring application. The proposed method performs at 0.83 in recall, 0.88 in precision and 0.85 in F-measure on the evaluation data set.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2020-05-01","The student, Duc Phan, accepted the attached license on 2018-02-22 at 10:07.","The student, Duc Phan, submitted this Thesis for approval on 2018-02-22 at 10:16.","This Thesis was approved for publication on 2018-02-23 at 11:06.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12045 on 2018-08-31 at 17:17:11","Made available in DSpace on 2018-09-04T20:33:48Z (GMT). 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